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Mutational heterogeneity in nonserous ovarian cancers Jamie K. Teer1, Sean Yoder2, Anxhela Gjyshi3,4, Santo V. Nicosia5, Chaomei Zhang2 & Alvaro N. A. Monteiro 3
Received: 1 November 2016 Accepted: 9 August 2017 Published: xx xx xxxx
Epithelial ovarian cancer is a leading cause of death in gynecological cancers. While several systematic studies have revealed the mutation landscape of serous epithelial ovarian cancer, other non-serous subtypes of the disease have not been explored as extensively. Here we conduct exome sequencing of nine non-serous epithelial ovarian tumors (six endometrioid and three mucinous) and their corresponding normal DNA as well as a tumor-only granulosa cell sample. We integrated the exome data with targeted gene sequencing for 1,321 genes selected for their involvement in cancer from additional 28 non-serous ovarian tumors and compared our results to TCGA ovarian serous cystadenocarcinoma and uterine corpus endometrial carcinomas. Prevalence of TP53 mutations in non-serous was much lower than in serous epithelial OC, whereas the prevalence of PIK3CA, PIK3R1, PTEN, CTNNB1, ARID1A, and KRAS was higher. We confirmed the high prevalence of FOXL2 and KRAS mutations in granulosa cell tumors and in mucinous tumors, respectively. We also identified POLE proofreading domain mutations in three endometrioid ovarian tumors. These results highlight mutational differences between serous and non-serous ovarian cancers, and further distinguish different non-serous subtypes. Epithelial Ovarian Cancer (EOC) is a heterogeneous disease with five major histologic types1, 2: high-grade serous carcinoma, which accounts for the approximately 70% of all EOC cases, and clear cell (10%), endometrioid (10%), mucinous (3%), and low-grade serous carcinomas ( TGT; Signature 10) (http://cancer.sanger.ac.uk/cosmic/ signatures)38 (Fig. 2A,B). This signature was visible in the tumor-only analysis despite the lower somatic mutation specificity we have previously observed (Teer et al. unpublished observation). In ES samples, we observed signature 1b “Age” (likely age-related cytosine-deamination) in all samples and signature 18, which has unknown etiology, as the predominant signature in two samples of different histologies (OV10: mucinous and OV6: endometrioid) (Fig. 3A,C).
Copy number alterations. Tumor specific copy number alterations (CNA) were examined in the ES data-
set. A median of 25 events was observed in each sample (range 5–192) covering a median of 76,303,334 bases (range 1,818,739–661,026,741) (Supplementary Table 7). Although many events were observed in OV7, we suspected these were unreliable due to the very low coverage in the sample from normal plasma, and did not consider this sample further. As many of the alterations were large (median = 236,356 bases, range 1,291–47,327,598 bases), genes that have been previously described as cancer drivers39 were identified in the CNA regions. Between 0 and 13 driver genes fell in copy number altered regions in each sample (Supplementary Table 8). Driver genes in amplified segments included FGFR3, MYC, KRAS, EXT1, and PTEN. Genes in deleted segments included STK11, NF2, CHEK2, PMS2, CDKN2A, and FANCA (Fig. 3). No sample contained a copy number alteration overlapping TP53, BRCA1 or BRCA2 (Fig. 3, Supplementary Table 8). Recent TCGA genomic classifications of high-grade serous ovarian adenocarcinoma and endometrial carcinomas revealed high levels of somatic CNAs in high grade serous ovarian and serous endometrial tumors, including recurrent amplifications in MYC, and CCNE1. We observed fewer CNAs in non-serous ovarian tumors, including only 2 samples with a MYC amplification and no samples with a CCNE1 amplification. Interestingly, sample OV3, an endometrioid tumor from the youngest individual in our cohort (diagnosed at 28 years of age) had the most CNAs. This tumor had the third lowest number of mutations with no identified common cancer mutations (Supplementary Table 4; Fig. 1). Altered driver genes in OV3 were mainly amplified, and included NOTCH2, ALK, PIK3R1, and MET. Two tumor suppressor genes were also amplified: PTEN and CDKN2A. Only one deletion was observed, which contained PMS2.
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Figure 3. Mutational landscape across non-serous ovarian tumors. Left: (A) Circos plot display detected somatic alterations in Exome Sequencing (ES) tumors. Right: From outmost to inner most ring, chromosome ideogram, number of samples with mutations (light grey, increasing towards outside), number of samples with amplification (light red, increasing towards outside), numbers of samples with deletion (light blue, increasing towards inside). Dark blue slices highlight recurrent features: counterclockwise from top-left: PIK3CA mutations, amplification in MYC region, lack of mutations or deletions in TP53 region.
Discussion
Here, we performed exome sequencing of ten incident cases of non-serous stage 1 ovarian neoplasms to determine somatic mutations and copy number alterations. We also performed target gene sequencing for 1,321 genes in 28 non-serous tumors to explore the mutation landscape of non-serous ovarian tumors. A limitation of our ES cohort study is a relatively small sample size and reported frequencies should be taken with caution. Despite this limitation, the availability of normal matched DNA allowed an improved identification of relevant variants. We also used an additional independent cohort. The data integration and comparison with publically available data led to five main conclusions. First, non-serous ovarian tumors seem to be distinct from serous subtypes in their prevalence of TP53 pathogenic variants. While previous studies have reported mutations in 96% of high grade serous epithelial tumors8, only one ES sample (OV8; mucinous) had a TP53 mutation and no samples had deletions in the region. We examined the fraction of bases with depth of coverage ≥ 10 across the TP53 gene, and found an average of 73.7% per sample, suggesting our coverage was sufficient to have observed mutations. In the TGS cohort, mutations were prevalent in mucinous (4/7, 57%), endometrioid (6/20, 30%) and clear cell (1/5, 20%) subtypes but still markedly lower than high grade serous. This is line with previous studies of clear cell carcinomas reporting 10–15% TP53 mutation frequency9, 40. Interestingly, comparative studies of TP53 mutations in uterine cancer also reports widely different mutation frequencies between the endometrioid (11.4–17%) and serous carcinomas (>90%)36, 41. Second, a small but significant fraction of endometrioid tumors display mutations in the proofreading domain of POLE, and mutations are not found in other non-serous subtypes. One ES sample (OV4; endometrioid) harbored a POLE mutation in the proofreading domain, and showed a mutational signature matching a previously reported signature of POLE deficiency38. In the TGS set we observed recurrent POLE mutations in 14% (2/14) of endometrioid ovarian cancers. A screen for POLE mutations in a study of 251 Chinese samples of different subtypes of ovarian carcinomas, identified mutations in 3 out of 37 (8.1%) patients with ovarian endometrioid carcinoma, but not observed in the other subtypes42. Interestingly, POLE hotspot mutations were found in 7% (17/248) of uterine carcinomas, all of endometrioid histology36. Third, we confirmed the high prevalence (5/6; 83%) of pathognomonic mutations in FOXL2 in adult granulosa cell tumors. This is consistent with existing reports showing a prevalence ranging from 70.4% to 100%43–50. Fourth, different non-serous subtypes have distinct mutation profiles. KRAS mutations are observed in significant fraction of mucinous and, to a lesser extent, in endometrioid but much less frequently (0.6%; 2/316) in high Scientific REPOrTS | 7: 9728 | DOI:10.1038/s41598-017-10432-9
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www.nature.com/scientificreports/ grade serous ovarian tumors8. A total of 71% (5/7) of mucinous samples carried mutations in KRAS, confirming its high frequency in this subtype51–53. We also observed 20% (4/20) of endometrioid tumors with mutations in KRAS. Mutations in PIK3CA and ARID1A are the most frequent alteration in clear cell carcinomas found in combination in 2/5 (40%) of all CCC tumors in the present study. Sequencing of 97 OCCC tumors identified a high frequency of mutations in PIK3CA (33%) and in TP53 (15%)9. Finally, the finding that deletion in genes implicated in the DNA damage response are relatively common suggest that therapeutic approaches targeting these pathways, such as the use of PARP inhibitors may be provide effective options in treatment. In summary, our results show that non-serous ovarian tumors are mutationally distinct from serous ovarian tumors. Interestingly, a similar but not identical mutational profile can be found in endometrioid ovarian and endometrioid uterine tumors. These similarities support the link between endometrioid ovarian cancers and endometriosis31. Our findings also offer molecular evidence to support continued distinction of non-serous ovarian cancers by histology, and may offer the opportunity for future work to investigate subtype-specific interventions targeting unique molecular alterations.
Materials and Methods
Participants. For the Exome Sequencing (ES) cohort all subjects were women between the ages of 28 and
63 (median 51) from Moffitt Cancer Center. Tumor and matched normal samples were retrieved from Moffitt’s Total Cancer Care tissue bank. Tumor samples for sequencing were selected to have >80% tumor cellularity. Six endometrioid, three mucinous, and one granulosa cell ovarian tumors were sequenced (Table 1). The study was approved by the Institutional Review board of the University of South Florida and all methods were performed in accordance with the relevant guidelines and regulations. For the Target Gene Sequencing (TGS) cohort all samples were from the Total Cancer Care tissue bank. Patients were women between the ages of 30 and 79 (median 60) diagnosed in 1993–2012. Tumors underwent macrodissection to achieve >80% tumor cellularity in subsequent molecular studies. Samples were grouped by subtype: fourteen endometrioid, four mucinous, five clear cell, and five granulosa cell tumors were target sequenced (Table 2). All patients in both cohorts have provided informed consent under the Total Cancer Care protocol.
Pathology review. We conducted a pathology review of all 10 cases used in exome sequencing. Details of the findings are presented in Table 1. Pathology reviews performed on hematoxylin and eosin (H&E) stained sections from a formalin-fixed paraffin-embedded (FFPE) block made during tissue acquisition at Moffitt’s Tissue Bank. Samples for sequencing were derived from the same dissected tumor samples used for FFPE and pathology review. For samples OV2, OV6, OV8, and OV9, original H&E sections were not available and new cuts were made from the original corresponding FFPE blocks and stained. In these cases it is possible that the section may not represent the local tumor section used for sequencing. Representative images are shown in Supplementary Figure 3. DNA isolation. DNAs from snap frozen ovarian tumor and from normal uterus were isolated using Qiagen DNeasy Blood & Tissue Kit (Qiagen). DNA from blood was isolated using Qiagen Gentra Puregene Blood Kit or Qiagen PAXgene blood DNA kit. Exome Sequencing. Paired-end libraries were constructed and whole exome capture was performed using
Roche NimbleGen SeqCap EZ v3 (Roche). Captured libraries were sequenced using 100 base-pair reads on an Illumina HiScanSQ platform (Illumina, Inc., San Diego, CA) following the manufacturer’s protocols. Reads were aligned to the human genome reference hs37d5 using the Burrows-Wheeler Aligner (BWA)54. The Genome Analysis ToolKit (GATK)55 was used for insertion/deletion realignment, and quality score recalibration. Somatic mutations were detected with either Strelka56 or MuTect57. Strelka 1.0.13 was run with default settings except the following: ssnvNoise = 0.00000005, sindelNoise = 0.0000001. MuTect 1.1.4 was run with default settings except: max_alt_alleles_in_normal_count = 3, max_alt_allele_in_normal_fraction = 0.05. ANNOVAR58 was used to annotate variants. Final read depth averaged 39.2×, and a median of ~87% of bases across samples had ≥10 reads. Coverage in each sample across genes of interest can be seen in Supplementary Figure 2. Copy number alterations were detected using EXCAVATOR59. Circos was used for visualization of somatic alterations60. Blood was unavailable for case OV7, and although plasma was subjected to exome capture, sequencing coverage was low (0.2% target bases having ≥10 reads) and duplicate rates were high (87%) preventing sensitive tumor/ normal mutation detection. Case OV7 was analyzed with GATK, and mutations were enriched by filtering out known variants. We also examined normal DNA to identify germline mutations implicated in ovarian cancer in breast/ovarian cancer families or in Lynch syndrome30, 61–63: ATM, BARD1, BRCA1, BRCA2, BRIP1, CDH1, CHEK2, MLH1, MRE11A, MSH2, MSH6, MUTYH, NBN, PALB2, PMS1, PMS2, PTEN, RAD50, RAD51C, RAD51D, STK11, and TP53.
Targeted Gene Sequencing. Target gene capture was performed using SureSelect custom designs (Agilent
Technologies, Inc., Santa Clara, CA) targeting 1,321 genes (Supplementary Table 2). Sequencing was performed using an Illumina GAIIx sequencing platform (Illumina, Inc., San Diego, CA) at the Beijing Genomics Institute (BGI, Shenzhen, China). Sequences were aligned to the human genome reference hs37d5 using the Burrows-Wheeler Aligner (BWA)54. The Genome Analysis ToolKit (GATK)55 was used for insertion/deletion realignment, quality score recalibration, and variant identification. ANNOVAR58 was used to annotate mutations. Matched normal samples were not available for comparison so somatic mutations were enriched via population filtering, including 1000 Genomes Scientific REPOrTS | 7: 9728 | DOI:10.1038/s41598-017-10432-9
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www.nature.com/scientificreports/ and 238 unmatched normal samples. Final read depth averaged ~140x across the targeted regions and a median of ~94% of bases across samples had ≥10 reads (Supplementary Figure 2).
TCGA mutation frequencies. Mutation frequencies were downloaded from cBioportal64, 65 using the “Ovarian Serous Cystadenocarcinoma8 and “Uterine Corpus Endometrial Carcinoma36.
Mutation signatures. Mutational signatures were derived from the ES samples using previously described methods38. Curated and updated ICGC (International Cancer Genomics Consortium) signatures can be found at http://cancer.sanger.ac.uk/cosmic/signatures.
References
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Acknowledgements
This work was supported by grants from the Phi Beta Psi Foundation, Ovarian Cancer Research Foundation (258807), and in part by the Cancer Informatics and the Molecular Genomics Core Facilities at the Moffitt Cancer Center through its NCI CCSG grant (P30-CA76292). We thank Ross Myers, Lori Kirchoff, Michelle Fournier, Vonetta Williams, and Marek Wloch for technical assistance.
Author Contributions
S.Y. and C.Z. generated data; J.K.T. and A.N.M. designed the study; J.K.T., A.G., S.V.N., and A.N.A.M. analyzed data and prepared the manuscript. All authors approved the manuscript.
Additional Information
Supplementary information accompanies this paper at doi:10.1038/s41598-017-10432-9 Competing Interests: The authors declare that they have no competing interests. Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. © The Author(s) 2017
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Mutational heterogeneity in nonserous ovarian cancers Jamie K. Teer1, Sean Yoder2, Anxhela Gjyshi3,4, Santo V. Nicosia5, Chaomei Zhang2 & Alvaro N. A. Monteiro 3
Received: 1 November 2016 Accepted: 9 August 2017 Published: xx xx xxxx
Epithelial ovarian cancer is a leading cause of death in gynecological cancers. While several systematic studies have revealed the mutation landscape of serous epithelial ovarian cancer, other non-serous subtypes of the disease have not been explored as extensively. Here we conduct exome sequencing of nine non-serous epithelial ovarian tumors (six endometrioid and three mucinous) and their corresponding normal DNA as well as a tumor-only granulosa cell sample. We integrated the exome data with targeted gene sequencing for 1,321 genes selected for their involvement in cancer from additional 28 non-serous ovarian tumors and compared our results to TCGA ovarian serous cystadenocarcinoma and uterine corpus endometrial carcinomas. Prevalence of TP53 mutations in non-serous was much lower than in serous epithelial OC, whereas the prevalence of PIK3CA, PIK3R1, PTEN, CTNNB1, ARID1A, and KRAS was higher. We confirmed the high prevalence of FOXL2 and KRAS mutations in granulosa cell tumors and in mucinous tumors, respectively. We also identified POLE proofreading domain mutations in three endometrioid ovarian tumors. These results highlight mutational differences between serous and non-serous ovarian cancers, and further distinguish different non-serous subtypes. Epithelial Ovarian Cancer (EOC) is a heterogeneous disease with five major histologic types1, 2: high-grade serous carcinoma, which accounts for the approximately 70% of all EOC cases, and clear cell (10%), endometrioid (10%), mucinous (3%), and low-grade serous carcinomas ( TGT; Signature 10) (http://cancer.sanger.ac.uk/cosmic/ signatures)38 (Fig. 2A,B). This signature was visible in the tumor-only analysis despite the lower somatic mutation specificity we have previously observed (Teer et al. unpublished observation). In ES samples, we observed signature 1b “Age” (likely age-related cytosine-deamination) in all samples and signature 18, which has unknown etiology, as the predominant signature in two samples of different histologies (OV10: mucinous and OV6: endometrioid) (Fig. 3A,C).
Copy number alterations. Tumor specific copy number alterations (CNA) were examined in the ES data-
set. A median of 25 events was observed in each sample (range 5–192) covering a median of 76,303,334 bases (range 1,818,739–661,026,741) (Supplementary Table 7). Although many events were observed in OV7, we suspected these were unreliable due to the very low coverage in the sample from normal plasma, and did not consider this sample further. As many of the alterations were large (median = 236,356 bases, range 1,291–47,327,598 bases), genes that have been previously described as cancer drivers39 were identified in the CNA regions. Between 0 and 13 driver genes fell in copy number altered regions in each sample (Supplementary Table 8). Driver genes in amplified segments included FGFR3, MYC, KRAS, EXT1, and PTEN. Genes in deleted segments included STK11, NF2, CHEK2, PMS2, CDKN2A, and FANCA (Fig. 3). No sample contained a copy number alteration overlapping TP53, BRCA1 or BRCA2 (Fig. 3, Supplementary Table 8). Recent TCGA genomic classifications of high-grade serous ovarian adenocarcinoma and endometrial carcinomas revealed high levels of somatic CNAs in high grade serous ovarian and serous endometrial tumors, including recurrent amplifications in MYC, and CCNE1. We observed fewer CNAs in non-serous ovarian tumors, including only 2 samples with a MYC amplification and no samples with a CCNE1 amplification. Interestingly, sample OV3, an endometrioid tumor from the youngest individual in our cohort (diagnosed at 28 years of age) had the most CNAs. This tumor had the third lowest number of mutations with no identified common cancer mutations (Supplementary Table 4; Fig. 1). Altered driver genes in OV3 were mainly amplified, and included NOTCH2, ALK, PIK3R1, and MET. Two tumor suppressor genes were also amplified: PTEN and CDKN2A. Only one deletion was observed, which contained PMS2.
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Figure 3. Mutational landscape across non-serous ovarian tumors. Left: (A) Circos plot display detected somatic alterations in Exome Sequencing (ES) tumors. Right: From outmost to inner most ring, chromosome ideogram, number of samples with mutations (light grey, increasing towards outside), number of samples with amplification (light red, increasing towards outside), numbers of samples with deletion (light blue, increasing towards inside). Dark blue slices highlight recurrent features: counterclockwise from top-left: PIK3CA mutations, amplification in MYC region, lack of mutations or deletions in TP53 region.
Discussion
Here, we performed exome sequencing of ten incident cases of non-serous stage 1 ovarian neoplasms to determine somatic mutations and copy number alterations. We also performed target gene sequencing for 1,321 genes in 28 non-serous tumors to explore the mutation landscape of non-serous ovarian tumors. A limitation of our ES cohort study is a relatively small sample size and reported frequencies should be taken with caution. Despite this limitation, the availability of normal matched DNA allowed an improved identification of relevant variants. We also used an additional independent cohort. The data integration and comparison with publically available data led to five main conclusions. First, non-serous ovarian tumors seem to be distinct from serous subtypes in their prevalence of TP53 pathogenic variants. While previous studies have reported mutations in 96% of high grade serous epithelial tumors8, only one ES sample (OV8; mucinous) had a TP53 mutation and no samples had deletions in the region. We examined the fraction of bases with depth of coverage ≥ 10 across the TP53 gene, and found an average of 73.7% per sample, suggesting our coverage was sufficient to have observed mutations. In the TGS cohort, mutations were prevalent in mucinous (4/7, 57%), endometrioid (6/20, 30%) and clear cell (1/5, 20%) subtypes but still markedly lower than high grade serous. This is line with previous studies of clear cell carcinomas reporting 10–15% TP53 mutation frequency9, 40. Interestingly, comparative studies of TP53 mutations in uterine cancer also reports widely different mutation frequencies between the endometrioid (11.4–17%) and serous carcinomas (>90%)36, 41. Second, a small but significant fraction of endometrioid tumors display mutations in the proofreading domain of POLE, and mutations are not found in other non-serous subtypes. One ES sample (OV4; endometrioid) harbored a POLE mutation in the proofreading domain, and showed a mutational signature matching a previously reported signature of POLE deficiency38. In the TGS set we observed recurrent POLE mutations in 14% (2/14) of endometrioid ovarian cancers. A screen for POLE mutations in a study of 251 Chinese samples of different subtypes of ovarian carcinomas, identified mutations in 3 out of 37 (8.1%) patients with ovarian endometrioid carcinoma, but not observed in the other subtypes42. Interestingly, POLE hotspot mutations were found in 7% (17/248) of uterine carcinomas, all of endometrioid histology36. Third, we confirmed the high prevalence (5/6; 83%) of pathognomonic mutations in FOXL2 in adult granulosa cell tumors. This is consistent with existing reports showing a prevalence ranging from 70.4% to 100%43–50. Fourth, different non-serous subtypes have distinct mutation profiles. KRAS mutations are observed in significant fraction of mucinous and, to a lesser extent, in endometrioid but much less frequently (0.6%; 2/316) in high Scientific REPOrTS | 7: 9728 | DOI:10.1038/s41598-017-10432-9
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www.nature.com/scientificreports/ grade serous ovarian tumors8. A total of 71% (5/7) of mucinous samples carried mutations in KRAS, confirming its high frequency in this subtype51–53. We also observed 20% (4/20) of endometrioid tumors with mutations in KRAS. Mutations in PIK3CA and ARID1A are the most frequent alteration in clear cell carcinomas found in combination in 2/5 (40%) of all CCC tumors in the present study. Sequencing of 97 OCCC tumors identified a high frequency of mutations in PIK3CA (33%) and in TP53 (15%)9. Finally, the finding that deletion in genes implicated in the DNA damage response are relatively common suggest that therapeutic approaches targeting these pathways, such as the use of PARP inhibitors may be provide effective options in treatment. In summary, our results show that non-serous ovarian tumors are mutationally distinct from serous ovarian tumors. Interestingly, a similar but not identical mutational profile can be found in endometrioid ovarian and endometrioid uterine tumors. These similarities support the link between endometrioid ovarian cancers and endometriosis31. Our findings also offer molecular evidence to support continued distinction of non-serous ovarian cancers by histology, and may offer the opportunity for future work to investigate subtype-specific interventions targeting unique molecular alterations.
Materials and Methods
Participants. For the Exome Sequencing (ES) cohort all subjects were women between the ages of 28 and
63 (median 51) from Moffitt Cancer Center. Tumor and matched normal samples were retrieved from Moffitt’s Total Cancer Care tissue bank. Tumor samples for sequencing were selected to have >80% tumor cellularity. Six endometrioid, three mucinous, and one granulosa cell ovarian tumors were sequenced (Table 1). The study was approved by the Institutional Review board of the University of South Florida and all methods were performed in accordance with the relevant guidelines and regulations. For the Target Gene Sequencing (TGS) cohort all samples were from the Total Cancer Care tissue bank. Patients were women between the ages of 30 and 79 (median 60) diagnosed in 1993–2012. Tumors underwent macrodissection to achieve >80% tumor cellularity in subsequent molecular studies. Samples were grouped by subtype: fourteen endometrioid, four mucinous, five clear cell, and five granulosa cell tumors were target sequenced (Table 2). All patients in both cohorts have provided informed consent under the Total Cancer Care protocol.
Pathology review. We conducted a pathology review of all 10 cases used in exome sequencing. Details of the findings are presented in Table 1. Pathology reviews performed on hematoxylin and eosin (H&E) stained sections from a formalin-fixed paraffin-embedded (FFPE) block made during tissue acquisition at Moffitt’s Tissue Bank. Samples for sequencing were derived from the same dissected tumor samples used for FFPE and pathology review. For samples OV2, OV6, OV8, and OV9, original H&E sections were not available and new cuts were made from the original corresponding FFPE blocks and stained. In these cases it is possible that the section may not represent the local tumor section used for sequencing. Representative images are shown in Supplementary Figure 3. DNA isolation. DNAs from snap frozen ovarian tumor and from normal uterus were isolated using Qiagen DNeasy Blood & Tissue Kit (Qiagen). DNA from blood was isolated using Qiagen Gentra Puregene Blood Kit or Qiagen PAXgene blood DNA kit. Exome Sequencing. Paired-end libraries were constructed and whole exome capture was performed using
Roche NimbleGen SeqCap EZ v3 (Roche). Captured libraries were sequenced using 100 base-pair reads on an Illumina HiScanSQ platform (Illumina, Inc., San Diego, CA) following the manufacturer’s protocols. Reads were aligned to the human genome reference hs37d5 using the Burrows-Wheeler Aligner (BWA)54. The Genome Analysis ToolKit (GATK)55 was used for insertion/deletion realignment, and quality score recalibration. Somatic mutations were detected with either Strelka56 or MuTect57. Strelka 1.0.13 was run with default settings except the following: ssnvNoise = 0.00000005, sindelNoise = 0.0000001. MuTect 1.1.4 was run with default settings except: max_alt_alleles_in_normal_count = 3, max_alt_allele_in_normal_fraction = 0.05. ANNOVAR58 was used to annotate variants. Final read depth averaged 39.2×, and a median of ~87% of bases across samples had ≥10 reads. Coverage in each sample across genes of interest can be seen in Supplementary Figure 2. Copy number alterations were detected using EXCAVATOR59. Circos was used for visualization of somatic alterations60. Blood was unavailable for case OV7, and although plasma was subjected to exome capture, sequencing coverage was low (0.2% target bases having ≥10 reads) and duplicate rates were high (87%) preventing sensitive tumor/ normal mutation detection. Case OV7 was analyzed with GATK, and mutations were enriched by filtering out known variants. We also examined normal DNA to identify germline mutations implicated in ovarian cancer in breast/ovarian cancer families or in Lynch syndrome30, 61–63: ATM, BARD1, BRCA1, BRCA2, BRIP1, CDH1, CHEK2, MLH1, MRE11A, MSH2, MSH6, MUTYH, NBN, PALB2, PMS1, PMS2, PTEN, RAD50, RAD51C, RAD51D, STK11, and TP53.
Targeted Gene Sequencing. Target gene capture was performed using SureSelect custom designs (Agilent
Technologies, Inc., Santa Clara, CA) targeting 1,321 genes (Supplementary Table 2). Sequencing was performed using an Illumina GAIIx sequencing platform (Illumina, Inc., San Diego, CA) at the Beijing Genomics Institute (BGI, Shenzhen, China). Sequences were aligned to the human genome reference hs37d5 using the Burrows-Wheeler Aligner (BWA)54. The Genome Analysis ToolKit (GATK)55 was used for insertion/deletion realignment, quality score recalibration, and variant identification. ANNOVAR58 was used to annotate mutations. Matched normal samples were not available for comparison so somatic mutations were enriched via population filtering, including 1000 Genomes Scientific REPOrTS | 7: 9728 | DOI:10.1038/s41598-017-10432-9
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www.nature.com/scientificreports/ and 238 unmatched normal samples. Final read depth averaged ~140x across the targeted regions and a median of ~94% of bases across samples had ≥10 reads (Supplementary Figure 2).
TCGA mutation frequencies. Mutation frequencies were downloaded from cBioportal64, 65 using the “Ovarian Serous Cystadenocarcinoma8 and “Uterine Corpus Endometrial Carcinoma36.
Mutation signatures. Mutational signatures were derived from the ES samples using previously described methods38. Curated and updated ICGC (International Cancer Genomics Consortium) signatures can be found at http://cancer.sanger.ac.uk/cosmic/signatures.
References
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Acknowledgements
This work was supported by grants from the Phi Beta Psi Foundation, Ovarian Cancer Research Foundation (258807), and in part by the Cancer Informatics and the Molecular Genomics Core Facilities at the Moffitt Cancer Center through its NCI CCSG grant (P30-CA76292). We thank Ross Myers, Lori Kirchoff, Michelle Fournier, Vonetta Williams, and Marek Wloch for technical assistance.
Author Contributions
S.Y. and C.Z. generated data; J.K.T. and A.N.M. designed the study; J.K.T., A.G., S.V.N., and A.N.A.M. analyzed data and prepared the manuscript. All authors approved the manuscript.
Additional Information
Supplementary information accompanies this paper at doi:10.1038/s41598-017-10432-9 Competing Interests: The authors declare that they have no competing interests. Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. © The Author(s) 2017
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